Hybrid adaptive index model for binary response data

Wan, Ke; Tanioka, Kensuke; Minami, Hiroyuki; Mizuta, Masahiro; Shimokawa, Toshio

Hybrid adaptive index model for binary response data

Wan, Ke; Tanioka, Kensuke; Minami, Hiroyuki et al.

Japanese Journal of Statistics and Data Science, 2021, 4(1), 299-315

Permalink : https://hdl.handle.net/2115/86180

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論文情報

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アクセス権	open access
URI	https://hdl.handle.net/2115/86180
タイトル	Hybrid adaptive index model for binary response data
著者	Wan, Ke ; Tanioka, Kensuke ; Minami, Hiroyuki ; Mizuta, Masahiro ; Shimokawa, Toshio
著者名 Wan, Ke
e-Rad_Researcher 40782818 著者名 Tanioka, Kensuke
e-Rad_Researcher 80261395 著者名 Minami, Hiroyuki
e-Rad_Researcher 70174026 著者名 Mizuta, Masahiro
e-Rad_Researcher 00402090 著者名 Shimokawa, Toshio
言語	英語
キーワード	Production rule
	Logistic regression model
	Controllable explanatory variables
	Uncontrollable explanatory variables
発行日	2021-07
出版者	Springer Nature
収録物名	Japanese Journal of Statistics and Data Science
巻(号)	4(1)
ページ	299-315
抄録	We often meet the case in data analysis that the explanatory variables can be occasionally divided into two groups. One group comprises the variables that researchers consider controllable, and the other group comprises those they do not. We call them controllable and uncontrollable variables, respectively. In the study, we deal with binary response data and aim to estimate the relationship between the binary response and controllable variables. Logistic regression model is typically used in binary response data. In addition to that, AIM (Adaptive Index Model; (Tian and Tibshirani Biostatics 12:68-86, 2010)) can also be used in binary response data. Contrast with logistic regression model, AIM can explain the result easier using binary rules but the prediction accuracy of AIM is shown worse than that of logistic regression model. Considering the interpretability and accuracy, it is better to apply AIM to controllable variables and adjust the effect of uncontrollable variables using logistic regression model. Therefore, we propose the method combining AIM and logistic regression model, called hybrid adaptive index model (HAIM), to give best solution.
資源タイプ	学術雑誌論文
出版タイプ	Accepted Manuscript
権利情報	This is a post-peer-review, pre-copyedit version of an article published in Japanese Journal of Statistics and Data Science. The final authenticated version is available online at: https://doi.org/10.1007/s42081-020-00097-6
PISSN	2520-8756
EISSN	2520-8764
関連情報 (isVersionOf)	出版社版DOI https://doi.org/10.1007/s42081-020-00097-6